Discrimination of Pulmonary Nodule Volume Change for Low- and High-contrast Tasks in a Phantom CT Study with Low-dose Protocols

Acad Radiol. 2019 Jul;26(7):937-948. doi: 10.1016/j.acra.2018.09.006. Epub 2018 Oct 3.

Abstract

Rationale and objectives: The quantitative assessment of volumetric CT for discriminating small changes in nodule size has been under-examined. This phantom study examined the effect of imaging protocol, nodule size, and measurement method on volume-based change discrimination across low and high object to background contrast tasks.

Materials and methods: Eight spherical objects ranging in diameter from 5.0 mm to 5.75 mm and 8.0 mm to 8.75 mm with 0.25 mm increments were scanned within an anthropomorphic phantom with either foam-background (high-contrast task, ∼1000 HU object to background difference)) or gelatin-background (low-contrast task, ∼50 to 100 HU difference). Ten repeat acquisitions were collected for each protocol with varying exposures, reconstructed slice thicknesses and reconstruction kernels. Volume measurements were obtained using a matched-filter approach (MF) and a publicly available 3D segmentation-based tool (SB). Discrimination of nodule sizes was assessed using the area under the ROC curve (AUC).

Results: Using a low-dose (1.3 mGy), thin-slice (≤1.5 mm) protocol, changes of 0.25 mm in diameter were detected with AU = 1.0 for all baseline sizes for the high-contrast task regardless of measurement method. For the more challenging low-contrast task and same protocol, MF detected changes of 0.25 mm from baseline sizes ≥5.25 mm and volume changes ≥9.4% with AUC≥0.81 whereas corresponding results for SB were poor (AUC within 0.49-0.60). Performance for SB was improved, but still inconsistent, when exposure was increased to 4.4 mGy.

Conclusion: The reliable discrimination of small changes in pulmonary nodule size with low-dose, thin-slice CT protocols suitable for lung cancer screening was dependent on the inter-related effects of nodule to background contrast and measurement method.

Keywords: lung nodule change analysis; minimum detectable change; quantitative imaging biomarkers; volumetric computed tomography.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Area Under Curve
  • Early Detection of Cancer / methods
  • Humans
  • Lung / diagnostic imaging
  • Lung Neoplasms / diagnostic imaging*
  • Lung Neoplasms / pathology
  • Phantoms, Imaging
  • ROC Curve
  • Radiation Dosage
  • Solitary Pulmonary Nodule / diagnostic imaging*
  • Solitary Pulmonary Nodule / pathology
  • Tomography, X-Ray Computed / methods*
  • Tumor Burden